11+ examples of Machine Learning in Healthcare

Machine learning (ML) is a subclass of Artificial Intelligence (AI) technology where the algorithms process big data sets to learn from them, detect patterns and execute tasks autonomously without being manually instructed. The widespread use of Artificial Intelligence and Machine Learning have automated various industries and businesses in recent years.

Among several such industries, the early digital transformation in the healthcare industry has led to the adoption of many advanced technologies, including AI/ML, cloud computing, robotics, etc. With the amount of data generated on one patient, Machine Learning algorithms have become a game-changer for healthcare, primarily due to two significant reasons:

High availability of medical data:

With data availability, implementing Machine Learning in healthcare has made everyday operations more manageable. This is probably one of the biggest reasons why healthcare was an early adopter of AI and Machine Learning .

Development of complex algorithms:

The development of complex algorithms led to the development of Machine Learning in the healthcare industry. Why? Because mainly the medical data is of very high dimension in character. It is vast and has thousands of attributes that can be examined thoroughly through Machine Learning.

Not only this, ML techniques in healthcare can be applied to solve a variety of tasks. These tasks include:

• Classification — ML can help determine and label the type of disease or medical case the patient is dealing with.
• Recommendations- Machine learning in medicine offers necessary medical information without actively searching for it.
• Clustering- Machine learning can help group together similar medical cases to analyze the patterns and conduct research in the future.
• Prediction- Using current data and expected trends, Machine Learning can predict how future events will unfold.
• Anomaly detection- using ML in healthcare can help you detect the factors that stand out from the common patterns and determine whether they require any actions to be performed.
• Automation- This is one of the significant applications of ML in healthcare. ML can positively manage standard repetitive tasks that take too much effort and time from patients and doctors. AI and ML can handle appointment scheduling, data entry, inventory management, etc.
• Ranking- ML in medicine can put the relevant information first, making the search for it easier.

The above applications merely overview benefits you can leverage from Machine Learning in healthcare. Not to forget the unlimited opportunities Machine Learning brings to the healthcare providers in case of staff reduction and predictive care. This is probably why the global market size of AI in healthcare is estimated to cross 28 billion USD by the year 2025.

Let us now narrow down the arena of broad Machine Learning use cases in healthcare.

11+ Examples of Machine Learning in Healthcare

Below are the most common and recent examples of machine learning in healthcare that are leveraged by patients, medical staff, and healthcare experts.

• Medical imaging

Object detection and image recognition are widely used in Magnetic Resonance (MR) and Computed Tomography (CT) processes. AI and Machine Learning help with disease detection, image segmentation, and prediction. Deep learning models in healthcare can create effective interpretations by combining aspects of imaging data.

For example, deep learning algorithms detect diabetic retinopathy, early Alzheimer’s symptoms, and ultrasound breast nodules. Thanks to new advances in Machine Learning, most radiology and pathology images can be investigated efficiently.

• Early detection of abnormalities

Machine learning algorithm use cases are widely applicable in simplifying complex data analysis, so abnormalities are determined and prioritized more precisely. The insights with convolutional neural networks (CNN) offer medical professionals accurate health issues and lead to early detection. This is how AI and ML are making the healthcare sector smarter with every advancement.

• Healthcare data analysis

Machine learning models can analyze electronic health records (EHR) that contain structured and unstructured data. Data such as diagnosis, clinical reports, laboratory test results, medication history, etc., can be studied thoroughly with accuracy at exceptional speed.

Moreover, smartphone applications and wearable technologies now have the potential to transform data to monitor risk factors for Machine Learning models. Your AI-based smartwatch probably updates your calorie intake, physical routine, heartbeat rate, and stress meter every minute, making it easier for you to track your health.

• Mental and physical health chatbots

The use of ML-based mental health applications such as YouCOMM, Woebot, Wisa is increasing. Some of these chatbot applications have leveraged Machine Learning models for urgency detection, more realistic diagnosis and conversations with patients.

In fact, recent studies suggest that automated chatbots can significantly decrease anxiety and depression in students and millennials by engaging them in their favorite activities.

• Personalized medicine

Usually, patients have a sleuth of conditions that require simultaneous treatment, which is why medicine is considered a resource-heavy field. ML in medicine helps simplify complex decisions by constructing a customized, effective treatment plan for patients accounting for drug interactions and minimizing potential side effects.

• Behavior adjustments

Experts say preventing diseases is as important in healthcare as treating conditions. One of the most underrated Machine Learning use cases in healthcare is modifying one’s behavior to establish a healthy lifestyle. Certain Machine Learning applications follow the patient’s daily activities and update them on their unconscious habits and routine so they can get rid of them.

• Timely attention to patients

NLP (Natural learning processing)-based applications support healthcare experts and patients by identifying and correcting possible diagnostic errors in prescriptions without much manual help. NLP also provides insights from free-text medical information for most appropriate medical treatments.

• Underwriting and fraud detection

Machine learning models help health insurance companies make potential and convenient offers to their customers by powerful predictive analytics. Moreover, one of the most prominent Machine Learning use cases in the healthcare insurance sector is identifying fraudulent claims and behavior by studying potential medical data.

• Decision making

AI/ML has played a vital role in decision-making by studying patients’ needs and evaluating any potential risk they might face. For instance, the use of surgical robots can minimize errors in surgeries and critical operations and provide various approaches to medical treatments. Another benefit is improved efficiency in surgical examinations. When major medical tasks are controlled by machines and robots, doctors can more likely focus on necessary treatments.

• Drug discovery

The contribution of AI and Machine learning in drug discovery and interaction prediction has been growing with recent advancements. ML in medical diagnosis identifies not only viable drug combinations but also processes clinical, genomic, and population data rapidly.

Research experts in pharmaceutical industries leverage Machine Learning toolkits to determine potential patterns in large data sets.

• Genomic analysis

Machine learning in healthcare also increases interpretability and offers a better understanding of medical and biological data. The complex data analysis capabilities of ML models support scientists in studying the interpretation of genome-based therapeutics development and certain genetic variations.

As mentioned in the above Machine Learning use case, CNN is widely used to catch attributes from DNA sequence windows.

• Covid-19

Last but not least, covid 19 has been a clear exhibit of the benefit of Machine Learning in healthcare. Deep learning and Machine Learning has helped with

• Early detection of covid 19 symptoms
• Finding potential patients with a higher risk of covid-19
• Estimating need for ventilation
• Predicting intensive care unit (ICU) admission

Wrapping Up

Machine learning in healthcare has led to improved workflow and operations, developed medical treatments, and innovated advanced solutions for the patients. Not just hospitals, several healthcare enterprises, and businesses have adopted Machine Learning by seeking professional ML software development solutions. We are looking forward to many such advancements that can save lives in the near future. ML software development solutions.